Tuğçe DURSUN, Yasemin SÜ, Rana COŞGUN, Ayşe Sevde DURAK, BARBAROS YET

COTTAPP: An Online University Timetable Application based on a Goal Programming Model

Preparing university course timetables is a challenging task as many constraints and requirements from the university and lecturers must be satisfied without overlapping courses for different student groups. Although many mathematical optimization models have been proposed to automate this task, a wider use of these models have been limited as deep technical understanding of mathematical and computer programming are required in order to use and implement them. This paper proposes a simple and flexible course timetabling application that is based on a weighted binary goal programming model with a powerful solver. Our application enables the users to modify and run this model by using a simple web and spreadsheet interface. Consequently, the model does not require deep technical understanding of the underlying models from its users even though it is based on a complex mathematical model. The web application and the underlying optimization model is illustrated by using a case study of an undergraduate program of industrial engineering

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1. Akkoyunlu, E.A., A linear algorithm for computing the optimum university timetable. The Computer Journal, 1973. 16(4): p. 347-350.

2. Schniederjans, M.J. and G.C. Kim, A goal programming model to optimize departmental preference in course assignments. Computers & Operations Research, 1987. 14(2): p. 87-96.

3. Dinkel, J.J., J. Mote, and M. Venkataramanan, Or practice— An efficient decision support system for academic course scheduling. Operations Research, 1989. 37(6): p. 853-864.

4. Dowsland, K.A., A timetabling problem in which clashes are inevitable. Journal of the Operational Research Society, 1990. 41(10): p. 907-918.

5. IBM. IBM CPLEX Optimizer. 2017 03/02/2017]; Available from: http://www.ibm.com/software/commerce/optimization/cplexoptimizer/.

6. Costa, D., A tabu search algorithm for computing an operational timetable. European Journal of Operational Research, 1994. 76(1): p. 98-110.

7. COTTAPP. COTTAPP: Course Timetabling Applicatoin. 2017; Available from: http://ieportal.hacettepe.edu.tr/apps/COTTApp/.

8. Badri, M.A., et al., A multi-objective course scheduling model: Combining faculty preferences for courses and times. Computers & operations research, 1998. 25(4): p. 303-316.

9. Colorni, A., M. Dorigo, and V. Maniezzo, Metaheuristics for high school timetabling. Computational optimization and applications, 1998. 9(3): p. 275-298.

10. Abramson, D., M.K. Amoorthy, and H. Dang, Simulated annealing cooling schedules for the school timetabling problem. Asia-Pacific Journal of Operational Research, 1999. 16(1): p. 1.

11. Deris, S., S. Omatu, and H. Ohta, Timetable planning using the constraint-based reasoning. Computers & Operations Research, 2000. 27(9): p. 819-840.

12. Burke, E.K. and S. Petrovic, Recent research directions in automated timetabling. European Journal of Operational Research, 2002. 140(2): p. 266-280.

13. Smith, K.A., D. Abramson, and D. Duke, Hopfield neural networks for timetabling: formulations, methods, and comparative results. Computers & industrial engineering, 2003. 44(2): p. 283-305.

14. Benli, O.S. and A. Botsali, An optimization-based decision support system for a university timetabling problem: An integrated constraint and binary integer programming approach. Computers and Industrial Engineering, 2004: p. 1- 29.

15. Carrasco, M.P. and M.V. Pato, A comparison of discrete and continuous neural network approaches to solve the class/teacher timetabling problem. European Journal of Operational Research, 2004. 153(1): p. 65-79.

16. Daskalaki, S., T. Birbas, and E. Housos, An integer programming formulation for a case study in university timetabling. European Journal of Operational Research, 2004. 153(1): p. 117-135.

17. Daskalaki, S. and T. Birbas, Efficient solutions for a university timetabling problem through integer programming. European Journal of Operational Research, 2005. 160(1): p. 106-120.

18. Al-Yakoob, S.M. and H.D. Sherali, Mathematical programming models and algorithms for a class–faculty assignment problem. European Journal of Operational Research, 2006. 173(2): p. 488-507.

19. MirHassani, S., A computational approach to enhancing course timetabling with integer programming. Applied Mathematics and Computation, 2006. 175(1): p. 814-822.

20. Bakır, M.A. and C. Aksop, A 0-1 integer programming approach to a university timetabling problem. Hacettepe Journal of Mathematics and Statistics, 2008. 37(1): p. 41-55.

21. Hao, J.-K. and U. Benlic, Lower bounds for the ITC-2007 curriculum-based course timetabling problem. European Journal of Operational Research, 2011. 212(3): p. 464-472.

22. R Development Core Team. R: A language and environment for statistical computing. 2017 03/02/2017]; Available from: http://www.R-project.org.

23. RStudio. Shiny: A web application framework for R. 2017 03/02/2017]; Available from: https://shiny.rstudio.com/.